ICML 2012 Conference and Workshops
ICML 2012 Oral Talks (International Conference on Machine Learning)ICML is the leading international machine learning conference and is supported by the International Machine Learning Society (IMLS). The 29th International Conference on Machine Learning (ICML 2012) will be held in Edinburgh, Scotland, on June 26th July 1, 2012.
ICML 2012 Workshop on New Challenges for Exploration & Exploitation 3The goal of this challenge is to build an algorithm that learns efficiently a policy to serve news articles on a web site. At each iteration of the evaluation process, you will be asked to pick an article from a list given a visitor (136 binary features + a timestamp). To build a smart algorithm, you might want to balance carefully exploration and exploitation and pay close attention to the “age” of the news articles (among other things of course). A quick look on the leaderboard is enough to figure out why that last point matters. It is the overall CTR (click through rate) of your algorithm that will be taken into account to rank it on the leaderboard.
ICML 2012 Workshop on Representation LearningIn this workshop we consider the question of how we can learn meaningful and useful representations of the data. There has been a great deal of recent work on this topic, much of it emerging from researchers interested in training deep architectures. Deep learning methods such as deep belief networks, sparse coding-based methods, convolutional networks, and deep Boltzmann machines, have shown promise as a means of learning invariant representations of data and have already been successfully applied to a variety of tasks in computer vision, audio processing, natural language processing, information retrieval, and robotics. Bayesian nonparametric methods and other hierarchical graphical model-based approaches have also been recently shown the ability to learn rich representations of data.
By bringing together researchers with diverse expertise and perspectives but who are all interested in the question of how to learn data representations, we will explore the challenges and promising directions for future research in this area.
Inferning 2012: ICML Workshop on interaction between Inference and LearningThis workshop studies the interactions between algorithms that learn a model, and algorithms that use the resulting model parameters for inference. These interactions are studied from two perspectives.
The first perspective studies how the choice of an inference algorithm influences the parameters the model ultimately learns. For example, many parameter estimation algorithms require inference as a subroutine. Consequently, when we are faced with models for which exact inference is expensive, we must use an approximation instead: MCMC sampling, belief propagation, beam-search, etc. On some problems these approximations yield superior models, yet on others, they fail catastrophically. We invite studies that analyze (both empirically and theoretically) the impact of approximate inference on model learning. How does approximate inference alter the learning objective? Affect generalization? Influence convergence properties? Further, does the behavior of inference change as learning continues to improve the quality of the model?
A second perspective from which we study these interactions is by considering how the learning objective and model parameters can impact both the quality and performance of inference during “test time.” These unconventional approaches to learning combine generalization to unseen data with other desiderata such as fast inference. For example, work in structured cascades learns model for which greedy, efficient inference can be performed at test time while still maintaining accuracy guarantees. Similarly, there has been work that learns operators for efficient search-based inference. There has also been work that incorporates resource constraints on running time and memory into the learning objective.
This workshop brings together practitioners from different fields (information extraction, machine vision, natural language processing, computational biology, etc.) in order to study a unified framework for understanding and formalizing the interactions between learning and inference. The following is a partial list of relevant keywords for the workshop:
- learning with approximate inference
- cost-aware learning
- learning sparse structures
- pseudo-likelihood training
- contrastive divergence
- piecewise training
- coarse to fine learning and inference
- scoring matching
- stochastic approximation
- incremental gradient methods and more ...
Object, functional and structured data: towards next generation kernel-based methods - ICML 2012 WorkshopThis workshop concerns analysis and prediction of complex data such as objects, functions and structures. It aims to discuss various ways to extend machine learning and statistical inference to these data and especially to complex outputs prediction. A special attention will be paid to operator-valued kernels and tools for prediction in infinite dimensional space.